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An original multi-criteria decision-making algorithm for solar panels selection in buildings

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  • El-Bayeh, Claude Ziad
  • Alzaareer, Khaled
  • Brahmi, Brahim
  • Zellagui, Mohamed
  • Eicker, Ursula

Abstract

Nowadays, there are several thousand solar panels (SP) in the market. These panels vary in size, efficiency, cost, warranty, technology, etc. Selecting the best SP becomes complicated when other factors are added to the list, such as the available roof area, weather conditions, electricity tariffs, energy consumptions, etc. Therefore, it becomes crucial to propose an algorithm that selects the best SP for a specific client and provides the best combination of quality-cost-satisfaction. This paper presents an original multi-criteria decision-making algorithm based on the concept of Rank-Weight-Rank to select the best SP by considering many criteria. For validation purposes, our method is compared to TOPSIS, and it shows advantages, especially regarding the simulation time and the accuracy of the selection. Our approach is faster than TOPSIS, and the speed of finding the solution increases exponentially when the number of criteria and alternatives increases. Moreover, it is reliable and accurate since the probability is more than 78% for similarity of 90%–100% between both methods in selecting the same best alternative. Results show that the highest SP efficiency and the most expensive ones are not the best alternatives, nor the lowest efficient neither the cheapest SPs are the worst alternatives.

Suggested Citation

  • El-Bayeh, Claude Ziad & Alzaareer, Khaled & Brahmi, Brahim & Zellagui, Mohamed & Eicker, Ursula, 2021. "An original multi-criteria decision-making algorithm for solar panels selection in buildings," Energy, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:energy:v:217:y:2021:i:c:s0360544220325032
    DOI: 10.1016/j.energy.2020.119396
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    References listed on IDEAS

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    1. Kharrazi, A. & Sreeram, V. & Mishra, Y., 2020. "Assessment techniques of the impact of grid-tied rooftop photovoltaic generation on the power quality of low voltage distribution network - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    2. Deyu Li & Gaston Heimeriks & Floor Alkemade, 2020. "The emergence of renewable energy technologies at country level: relatedness, international knowledge spillovers and domestic energy markets," Industry and Innovation, Taylor & Francis Journals, vol. 27(9), pages 991-1013, October.
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    Cited by:

    1. Claude Ziad El-Bayeh & Mohamed Zellagui & Navid Shirzadi & Ursula Eicker, 2021. "A Novel Optimization Algorithm for Solar Panels Selection towards a Self-Powered EV Parking Lot and Its Impact on the Distribution System," Energies, MDPI, vol. 14(15), pages 1-28, July.
    2. Peng, Honggang & Xiao, Zhi & Wang, Jianqiang & Li, Jian, 2021. "A decision support framework for new energy selection in rural areas from the perspectives of information reliability and criterion non-compensation," Energy, Elsevier, vol. 235(C).
    3. Mario Fargnoli & Emilio Salvatori & Massimo Tronci, 2024. "A Green Marketing and Operations Management Decision-Making Approach Based on QFDE for Photovoltaic Systems," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    4. Youssef Kassem & Hüseyin Gökçekuş & Ali Güvensoy, 2021. "Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus," Energies, MDPI, vol. 14(22), pages 1-27, November.

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